RTLS systems estimate locations for moving tags within a floor plan of interior rooms, in buildings such as hospitals. Many existing RTLS systems based on radio-frequency signals such as Wi-Fi or BLE, are designed to have moving tags that transmit a radio message, in a field of receiving devices called gateways, sensors, bridges, or Access Points. The network of gateways will use received signal strength of radio transmissions from a tag, as a proxy for estimating the distance between the tag and each gateway and use proximity or multi-lateration algorithms to estimate the locations of tags.
These approaches having tags that transmit, and location engines that are based only on multi-lateration, are standard in the industry, and provide location estimates that are acceptable for may use cases in industrial and manufacturing environments. But they fail to provide a highly accurate, room-level location fix for the environments that need to estimate which room an asset resides in, like hospitals.
RTLS systems in current use feature tags that wirelessly transmit advertisements into a field of fixed receivers, often named sensors, gateways or bridges. They attempt to locate tags by estimating a location on a floor plan (known as an (x,y) location fix for the map coordinates). Through a locating process known as multi-lateration, the one or more bridges measure the received signal strength (RSSI) of the advertisement they hear from a tag and forward that RSSI to a location engine. The location engine uses the received signal strength as an estimate of the distance between the tag and each reporting bridge, and the multi-lateration algorithm estimates the location of the tag on a floor plan by reporting the location as an (x,y) location on the floor plan. The distance between the estimated (x,y) location of the tag and its true (x,y) location on the floor plan may be called the “error”. Current RTLS vendors measure their typical error (or “typical accuracy”) in feet or meters. The typical error of an RTLS system is defined by a statistical population distribution of a large number of sample location estimates and their “error” measurements. Hence, RTLS-equipment vendors will often state their “typical error” or “typical accuracy” with phrases like “We are achieving 1-meter accuracy 90% of the time”.
These current systems and methods of locating asset, patient and staff tags are insufficient for some hospital use cases. For example, often people and assets are located in two adjacent rooms in a hospital. One room is used to store clean equipment and the other used to store soiled equipment. Nurses need to use clean equipment to serve patients, of course, and never use soiled equipment. The clean equipment is often stored on shelves in the clean-equipment room, so imagine 50 pieces of clean equipment on a shelf that is attached to the wall that is shared with the soiled-equipment room. The adjacent soiled-equipment room also has shelves, on the opposite side of the wall, containing 50 pieces of soiled equipment. The assets all sit on shelves about six inches from the shared wall, so they are within 12 inches of the adjacent room. In this example, all 100 pieces of equipment have attached RTLS tags. The challenge of the RTLS system is to locate each piece of equipment and reliably tell the nurses which equipment is in the clean room (so it can be used) versus which equipment is in the soiled room (which cannot be used on a patient until it is cleaned and moved to the clean room).
An RTLS which uses only radio signal strength will almost always fail to discriminate the precise room-location of all 100 pieces of equipment. It may be able to locate each tag to within one meter of its true location, but it cannot tell whether the asset is one meter to the left of the wall, or one meter to the right of the wall, so it misplaces the room estimate for some assets. Thus, new solutions are required to better locate and track these assets.
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